LETTER TO THE EDITOR Risk of re-identification of
Bibliographic record
Abstract
rk ti th d ak ease status and uniquely identify individuals. While we agree that the limits of data anonymization are now bet-methylomes, but for any other trait, we do not currently have unequivocal evidence of health or exposure data be-Joly et al. Clinical Epigenetics (2015) 7:45 DOI 10.1186/s13148-015-0079-zpoorly correlated with most changes and some sites revertH3A 0G1, Canada Full list of author information is available at the end of the articleter understood following numerous research papers on this topic, it should be understood that most of this re-search refers to hypothetical scenarios leading to assess-ments of low risk of re-identification [2,3]. Moreover, as ing easily read from these data. Results shown by Figure one in Philibert et al. show far from perfect prediction, even given homogeneous sampling to call differences be-tween smokers and nonsmokers in a controlled experi-ment. The result is not unexpected since recent studies [8] have shown a complex relationship with blood methy-lation and smoking, where the intensity of exposure is
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".